A comprehensive 2023 roadmap for Venus exploration synthesizing open questions about the planet’s evolution from potentially habitable to extreme greenhouse state, detailing the coordinated VERITAS, DAVINCI, and EnVision missions planned for the 2030s and identifying future technology requirements for answering fundamental habitability questions.
A deep dive into the physical limits of life on Venus, reviewing Charles Cockell’s foundational 1999 analysis while connecting it to modern discoveries like the 2020 phosphine detection and upcoming DAVINCI+ missions.
A fault-tolerant RDKit wrapper treating molecular visualization as a software engineering problem, implementing strategy pattern for SVG generation with automatic raster fallback, native SELFIES support for generative AI workflows, and strict type safety for reliable batch processing of millions of molecules in training pipelines.
Kingma and Welling’s foundational 2013 paper introducing Variational Autoencoders and the reparameterization trick, enabling end-to-end gradient-based training of generative models with continuous latent variables by making the sampling operation differentiable through a clever mathematical transformation.
Discover how Importance Weighted Autoencoders (IWAEs) use the same architecture as VAEs with a fundamentally more powerful objective to leverage multiple samples effectively.
Burda et al.’s ICLR 2016 paper introducing Importance Weighted Autoencoders, which use importance sampling to derive a strictly tighter log-likelihood lower bound than standard VAEs, addressing posterior collapse and improving generative quality. The model architecture remains the same.
A comprehensive 2020 analysis of the tautomerism problem in chemical databases, introducing 86 new tautomeric transformation rules and proposing algorithmic improvements for InChI V2 to recognize when different molecular representations are the same molecule in different tautomeric states.
A comprehensive 2013 review explaining how InChI emerged as the global standard for chemical structure identifiers, covering its history as a response to the Internet’s need for non-proprietary molecular linking, its governance under IUPAC, and the technical layers that ensure uniqueness across diverse chemical databases.
A 2025 Faraday Discussions paper describing the major overhaul of InChI v1.07 that fixed thousands of bugs, added robust support for inorganic and organometallic compounds, and modernized the system to align with FAIR data principles for chemistry databases.
A 2019 format specification introducing two complementary standards for chemical mixtures. Mixfile provides comprehensive mixture descriptions and MInChI provides compact canonical identifiers. This addresses the long-standing lack of standardized machine-readable formats for multi-component chemical systems.
Can we create a SMILES-like notation for nanomaterials? A collaborative workshop tackles the challenge of representing complex, multi-component nanomaterials with a proposed extension to the established InChI system.
A 2023 software update paper documenting major improvements to the SELFIES Python library, including architectural redesign using directed molecular graphs for faster performance, expanded chemical feature support, semantic constraints for validity, and user-friendly customization APIs that transform SELFIES from proof-of-concept into production-ready tool.